Prof. Dr. Frederick Klauschen
Prof. Dr. Frederick Klauschen
Research Group Lead / Charité
Research Grouplead | BIFOLD
Director | Pathologisches Institut, Ludwig-Maximilian-Universität München
Group Leader
Institute of Pathology
Charité UNIVERSITÄTSMEDIZIN BERLIN
| 2012 | Novartis Pathology-Oncology Award |
| 2011 | Human Frontier Science Program Young Investigator Award |
| 2004 | NIH Postdoctoral Fellowship Award |
Systems biological integration of proteogenomic profiles and histological images through bioinformatics and machine learning with the goal to better understand and predict pathological mechanisms in tumors and finally, to better diagnose and treat cancer.
- German Pathological Society
- International Academy of Pathology
- German Physical Society
Marie-Lisa Eich, Kai Standvoss, Timo Milbich, Alexander Möllers, Miriam Hägele, Philipp Anders, Lars Tharun, Hanna Kontradiuk, Sebastian Kons, Nader Aldoj, Recepcan Adigüzel, Adam Narai, Lukas Hönig, Jonathan Striebel, Binru Yang, Mihnea P. Dragomir, Marvin Sextro, Philipp Keyl, Philipp Jurmeister, Rosemarie Krupar, Evelyn Ramberger, James Wells, Julika Ribbat-Idel, Andreas Kunft, Hussam Shuaib, Christian Grohé, Reinhard Büttner, David Horst, Klaus-Robert Müller, Lukas Ruff, Maximilian Alber, Frederick Klauschen, Simon Schallenberg
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
Philipp Keyl, Niklas Kiermeyer, Jonah Bosserhoff, Tim Lenfers, Thibault Niederhauser, Bowen Fan, Thomas Schnake, Simon Schallenberg, Fabio Aubele, Solveig Kuss, Mina Jamshidi Idaji, Philipp Jurmeister, Moon Kim, Sebastian Bauer, Nikolaos Bechrakis, Michael Forsting, Dagmar Führer-Sakel, Martin Glas, Viktor Grünwald, Boris Hadaschik, Ken Herrmann, Stefan Kasper, Rainer Kimmig, Stephan Lang, Ina Pretzell, Tienush Rassaf, Alexander Roesch, Jens T. Siveke, Maja Guberina, Ulrich Sure, Marc Wichert, Michael Ingrisch, Kristian Unger, Jürgen Behr, Daniel Teupser, Christian G. Stief, Julia Mayerle, Nadia Harbeck, Amanda Tufman, Jens Ricke, Lars H. Lindner, Siegfried Priglinger, Günter Höglinger, Sven Mahner, Martin Canis, Lucie Heinzerling, Christine Spitzweg, Alpaslan Tasdogan, Matthias Totzeck, Anja Welt, Marcel Wiesweg, C. Benedikt Westphalen, Reinhard Thasler, Fady Albashiti, Grégoire Montavon, Nicola Miglino, Zsolt Balázs, Michael von Bergwelt-Baildon, Volker Heinemann, Claus Belka, Sylvia Hartmann, Andreas Wicki, Felix Nensa, Dirk Schadendorf, Michael Krauthammer, Klaus-Robert Müller, Martin Schuler, Frederick Klauschen, Jens Kleesiek, Julius Keyl
An explainable transformer model learning from entire treatment timelines for pan-cancer risk profiling across healthcare systems
Jonah Kömen, Edwin D. de Jong, Julius Hense, Hannah Marienwald, Jonas Dippel, Philip Naumann, Eric Marcus, Lukas Ruff, Maximilian Alber, Jonas Teuwen, Frederick Klauschen, Klaus-Robert Müller
Towards Robust Foundation Models for Digital Pathology
Philipp Anders, Marvin Sextro, Katja Lingelbach, Kai Standvoss, Suhas Pandhe, Sandip Ghosh, Cornelius Böhm, Stephan Tietz, Rosemarie Krupar, Lars Tharun, Marie-Lisa Eich, Julika Ribbat-Idel, Evelyn Ramberger, Xizi Liang, Verena Aumiller, Sabine Merkelbach-Bruse, Alexander Quaas, Nikolaj Frost, Georg Schlachtenberger, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens-Carsten Rückert, Reinhard Büttner, Christian Grohe, David Horst, Maximilian Alber, Lukas Ruff, Frederick Klauschen, Gabriel Dernbach, Philipp Seegerer, Simon Schallenberg
ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes
Philipp Jurmeister, Susanne Flach, Linda Bergmayr, Konstanze Schleich, Edgar Chimal Calderon, Liliana H Mochmann, Yauheniya Zhdanovich, Doreen Klingler, Ada Pusztai, Anna Kübler, Christoph Walz, Christoph Benedikt Westphalen, Alexander Beck, Maximilian Leitheiser, Gerben E Breimer, Johannes A Rijken, Lot Devriese, Philipp Baumeister, Alena Skálová, Simon Schallenberg, Frederick Klauschen, Andreas Mock
Spatially resolved ex vivo drug response profiling in SMARCB1-deficient sinonasal carcinoma
A benchmark for trustworthy clinical AI
A new study published in Nature Communications shows that today's pathology foundation models can be influenced by the origin of a tissue sample. Researchers at BIFOLD and Aignostics developed PathoROB, a first-of-its-kind benchmark to measure and reduce this bias, shaping how the next generation of pathology AI is built.
AI Improves Lung Cancer Diagnostics
An interdisciplinary research team from BIFOLD (Berlin Institute for the Foundations of Learning and Data), Technische Universität Berlin, Universitätsklinikum Köln, Charité - Universitätsmedizin Berlin, the AI company Aignostics, and Ludwig Maximilians University Munich (LMU) has developed a novel AI-based method to more accurately predict the survival of lung cancer patients.
AI in medicine: new approach for more efficient diagnostics
Researchers from LMU, BIFOLD, and Charité have developed a new AI tool that uses imaging data to also detect less frequent diseases of the gastrointestinal tract. In contrast to conventional models, the new AI only needs training data from common findings to detect deviations.
AI facilitates breakthrough in cancer diagnostics
So-called sinonasal undifferentiated carcinomas (SNUCs) are extremely difficult to diagnose. An interdisciplinary team of researchers has developed an AI tool that reliably distinguishes tumors on the basis of chemical DNA modifications
An overview of the current state of research in BIFOLD
Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.